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Archive of posts filed under the Causal Inference category.

Let’s try this again: It is nonsense to say that we don’t know whether a specific weather event was affected by climate change. It’s not just wrong, it’s nonsensical.

This post is by Phil Price, not Andrew. If you write something and a substantial number of well-intentioned readers misses your point, the problem is yours. Too many people misunderstood what I was sayinga few days ago in the post “There is no way to prove that [an extreme weather event] either was, or was […]

More golf putting, leading to a discussion of how prior information can be important for an out-of-sample prediction or causal inference problem, even if it’s not needed to fit existing data

Steve Stigler writes: I saw a piece on your blog about putting. It suggests to me that you do not play golf, or you would not think this was a model. Length is much more important than you indicate. I attach an old piece by a friend that is indeed the work of a golfer! […]

“There is no way to prove that [an extreme weather event] either was, or was not, affected by global warming.”

This post is by Phil, not Andrew. It’s hurricane season, which means it’s time to see the routine disclaimer that no single weather event can be attributed to global warming. There’s a sense in which that is true, and a sense in which it is very wrong. I’ll start by going way back to 2005. […]

“I feel like the really solid information therein comes from non or negative correlations”

Steve Roth writes: I’d love to hear your thoughts on this approach (heavily inspired by Arindrajit Dube’s work, linked therein): This relates to our discussion from 2014: My biggest takeaway from this latest: I feel like the really solid information therein comes from non or negative correlations: • It comes before • But it doesn’t […]

“Beyond ‘Treatment Versus Control’: How Bayesian Analysis Makes Factorial Experiments Feasible in Education Research”

Daniel Kassler, Ira Nichols-Barrer, and Mariel Finucane write: Researchers often wish to test a large set of related interventions or approaches to implementation. A factorial experiment accomplishes this by examining not only basic treatment–control comparisons but also the effects of multiple implementation “factors” such as different dosages or implementation strategies and the interactions between these […]

Multilevel Bayesian analyses of the growth mindset experiment

Jared Murray, one of the coauthors of the Growth Mindset study we discussed yesterday, writes: Here are some pointers to details about the multilevel Bayesian modeling we did in the Nature paper, and some notes about ongoing & future work. We did a Bayesian analysis not dissimilar to the one you wished for! In section […]

“Study finds ‘Growth Mindset’ intervention taking less than an hour raises grades for ninth graders”

I received this press release in the mail: Study finds ‘Growth Mindset’ intervention taking less than an hour raises grades for ninth graders Intervention is first to show national applicability, breaks new methodological ground – Study finds low-cost, online growth mindset program taking less than an hour can improve ninth graders’ academic achievement – The […]

Causal inference workshop at NeurIPS 2019 looking for submissions

Nathan Kallus writes: I wanted to share an announcement for a causal inference workshop we are organizing at NeurIPS 2019. I think the readers of your blog would be very interested, and we would be eager to have them interact/attend/submit. And here it is: The NeurIPS 2019 Workshop on “Do the right thing”: machine learning […]

Causal Inference and Generalizing from Your Data to the Real World (my talk tomorrow, Sat., 6pm in Berlin)

For the Berlin Bayesians meetup, organized by Eren Elçi: Causal Inference and Generalizing from Your Data to the Real World Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University Learning from data involves three stages of extrapolation: from sample to population, from treatment group to control group, and from measurement to the […]

Concerned about demand effects in psychology experiments? Incorporate them into the design.

Johannes Haushofer sends along this article with Jonathan de Quidt and Christopher Roth, “Measuring and Bounding Experimenter Demand,” which begins: We propose a technique for assessing robustness to demand effects of findings from experiments and surveys. The core idea is that by deliberately inducing demand in a structured way we can bound its influence. We […]

Gendered languages and women’s workforce participation rates

Rajesh Venkatachalapathy writes: I recently came across a world bank document claiming that gendered languages reduce women’s labor force participation rates. It is summarized in the following press release: Gendered Languages May Play a Role in Limiting Women’s Opportunities, New Research Finds. This sounds a lot like the piranha problem, if there is any effect […]

Causal inference using repeated cross sections

Sadish Dhakal writes: I am struggling with the problem of conditioning on post-treatment variables. I was hoping you could provide some guidance. Note that I have repeated cross sections, not panel data. Here is the problem simplified: There are two programs. A policy introduced some changes in one of the programs, which I call the […]

“Did Austerity Cause Brexit?”

Carsten Allefeld writes: Do you have an opinion on the soundness of this study by Thiemo Fetzer, Did Austerity Cause Brexit?. The author claims to show that support for Brexit in the referendum is correlated with the individual-level impact of austerity measures, and therefore possibly caused by them. Here’s the abstract of Fetzer’s paper: Did […]

Causal inference with time-varying mediators

Adan Becerra writes to Tyler VanderWeele: I have a question about your paper “Mediation analysis for a survival outcome with time-varying exposures, mediators, and confounders” that I was hoping that you could help my colleague (Julia Ward) and me with. We are currently using Medicare claims data to evaluate the following general mediation among dialysis […]

What if that regression-discontinuity paper had only reported local linear model results, and with no graph?

We had an interesting discussion the other day regarding a regression discontinuity disaster. In my post I shone a light on this fitted model: Most of the commenters seemed to understand the concern with these graphs, that the upward slopes in the curves directly contribute to the estimated negative value at the discontinuity leading to […]

Another Regression Discontinuity Disaster and what can we learn from it

As the above image from Diana Senechal illustrates, a lot can happen near a discontinuity boundary. Here’s a more disturbing picture, which comes from a recent research article, “The Bright Side of Unionization: The Case of Stock Price Crash Risk,” by Jeong-Bon Kim, Eliza Xia Zhang, and Kai Zhong: which I learned about from the […]

How to simulate an instrumental variables problem?

Edward Hearn writes: In an effort to buttress my own understanding of multi-level methods, especially pertaining to those involving instrumental variables, I have been working the examples and the exercises in Jennifer Hill’s and your book. I can find general answers at the Github repo for ARM examples, but for Chapter 10, Exercise 3 (simulating […]

Causal inference: I recommend the classical approach in which an observational study is understood in reference to a hypothetical controlled experiment

Amy Cohen asked me what I thought of this article, “Control of Confounding and Reporting of Results in Causal Inference Studies: Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals,” by David Lederer et al. I replied that I liked some of their recommendations (downplaying p-values, graphing raw data, presenting results clearly) […]

“Did Jon Stewart elect Donald Trump?”

I wrote this post a couple weeks ago and scheduled it for October, but then I learned from a reporter that the research article under discussion was retracted, so it seemed to make sense to post this right away while it was still newsworthy. My original post is below, followed by a post script regarding […]

Did blind orchestra auditions really benefit women?

You’re blind! And you can’t see You need to wear some glasses Like D.M.C. Someone pointed me to this post, “Orchestrating false beliefs about gender discrimination,” by Jonatan Pallesen criticizing a famous paper from 2000, “Orchestrating Impartiality: The Impact of ‘Blind’ Auditions on Female Musicians,” by Claudia Goldin and Cecilia Rouse. We’ve all heard the […]